Point-of-Care Guides

Applying a Clinical Decision Rule for CAD in Primary Care to Select a Diagnostic Test and Interpret the Results

 

Am Fam Physician. 2019 May 1;99(9):584-586.

Clinical Question

When a patient presents with chest pain, how should the primary care physician use the patient’s history to decide the next steps?

Evidence Summary

Chest pain is one of the most common presenting symptoms in primary care. Three prospective studies in the primary care setting found that among patients with chest pain, 10.5% to 11.3% had stable angina and 1.5% to 3.7% had acute cardiac ischemia.1 Other serious causes of chest pain are less common in the primary care setting.

Based on the patient history, the physician must make a subjective judgment about the likelihood of coronary artery disease (CAD) as the cause and then decide whether to perform a diagnostic test. In many cases, the diagnosis is clearly not CAD (e.g., pain when pressing the chest and no characteristics of CAD in a young patient), but the history is often equivocal. In such cases, diagnostic tests may be valuable, but the potential for misleading results (e.g., false-negative results when the pretest probability of CAD is very high, false-positive results when the pretest probability is very low) complicates the interpretation of test results.

Since 1990, five clinical decision rules for interpreting the history in patients with chest pain in primary care have been published. Calling their collaboration INTERCHEST, the authors of these rules agreed to combine the data from their studies to develop a new chest pain clinical decision rule for primary care.2  Their statistical analysis identified five predictors of CAD; each was assigned a weight of +1 when present and zero when absent. A sixth finding (localized pain when pressing the chest wall) reduced the probability of CAD, so this finding was assigned a weight of –1 when present (Table 1).2

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TABLE 1.

Chest Pain Clinical Decision Rule

Clinical predictorPoints

Pain reproduced by palpating the chest wall

−1

Older age (men ≥ 55 years; women ≥ 65 years)

1

Physician initially suspected a serious condition

1

Chest discomfort feels like “pressure”

1

Chest pain related to effort

1

History of coronary artery disease

1

Total (range −1 to 5):

____


Information from reference 2.

TABLE 1.

Chest Pain Clinical Decision Rule

Clinical predictorPoints

Pain reproduced by palpating the chest wall

−1

Older age (men ≥ 55 years; women ≥ 65 years)

1

Physician initially suspected a serious condition

1

Chest discomfort feels like “pressure”

1

Chest pain related to effort

1

History of coronary artery disease

1

Total (range −1 to 5):

____


Information from reference 2.

In Figure 125  (a graph based on the numbers in eTable A), the gray bar shows the pretest probability of CAD for each chest pain score in an INTERCHEST study site, in which the overall prevalence of CAD was 13% in patients presenting for evaluation of chest pain. The figure also shows—for each chest pain score—the posttest probabilities of CAD after stress electrocardiography (ECG), single-photon emission computed tomography (SPECT), or computed tomography (CT) angiography. The bars to the left of the gray bar denote the probability of CAD after a negative test result, and the bars to the right of the gray bar denote the probability of CAD after a positive result. The vertical black lines on each bar denote the 95% CIs, which reflect uncertainty in the chest pain score 2 and in the sensitivity and specificity of stress ECG, SPECT, and CT angiography.3,4

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FIGURE 1.

This graph shows the probabilities of coronary artery disease (CAD) corresponding to the chest pain score (Table 1). The gray bar shows the pretest probability. Bars to the left of the gray bar show the probability of CAD after a negative result on stress electrocardiography (ECG), single-photon emission computed tomography (SPECT), or computed tomography (CT) angiography. Bars to the right of the gray bar show the probability of CAD after a positive result on these tests. This graph is based on the numbers in eTable A.

Information from references 2 through 5.


FIGURE 1.

This graph shows the probabilities of coronary artery disease (CAD) corresponding to the chest pain score (Table 1). The gray bar shows the pretest probability. Bars to the left of the gray bar show the probability of CAD after a negative result on stress electrocardiography (ECG), single-photon emission computed tomography (SPECT), or computed tomography (CT) angiography. Bars to the right of the gray bar show the probability of CAD after a positive result on these tests. This graph is based on the numbers in eTable A.

Information from references 2 through 5.

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eTABLE A Pretest and Posttest Probabilities of CAD

Chest pain score (Table 1)
Parameter–1012345

Pretest

CAD prevalence (patients with CAD/total patients)*

Address correspondence to Harold C. Sox, MD, at hsox@comcast.net. Reprints are not available from the authors.

Author disclosure: No relevant financial affiliations.

References

show all references

1. Ebell MH. Evaluation of chest pain in primary care patients. Am Fam Physician. 2011;83(5):603–605....

2. Aerts M, Minalu G, Bösner S, et al.; International Working Group on Chest Pain in Primary Care (INTERCHEST). Pooled individual patient data from five countries were used to derive a clinical prediction rule for coronary artery disease in primary care. J Clin Epidemiol. 2017;81:120–128.

3. Knuuti J, Ballo H, Juarez-Orozco LE, et al. The performance of non-invasive tests to rule-in and rule-out significant coronary artery stenosis in patients with stable angina: a meta-analysis focused on post-test disease probability. Eur Heart J. 2018;39(35):3322–3330.

4. Schuetz M, Schlattmann P, Dewey M. Use of 3×2 tables with an intention to diagnose approach to assess clinical performance of diagnostic tests. Meta-analytical evaluation of coronary CT angiography studies. BMJ. 2012;345:e6717.

5. Inference for a population proportion In: Cowles MK. Applied Bayesian Statistics New York, NY: Springer; 2013.

This guide is one in a series that offers evidence-based tools to assist family physicians in improving their decision-making at the point of care.

This series is coordinated by Mark H. Ebell, MD, MS, Deputy Editor for Evidence-Based Medicine.

A collection of Point-of-Care Guides published in AFP is available at https://www.aafp.org/afp/poc.

 

 

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